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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Short summary
Although widely used, air sensors measurements are often biased. In this work we develop a correction with a relative humidity term that reduces the bias and improves consistency between different United States regions. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM2.5 measurements across the U.S. in the AirNow Fire and Smoke Map and has the potential to be successfully used in other air quality and public health applications.
Preprints
https://doi.org/10.5194/amt-2020-413
https://doi.org/10.5194/amt-2020-413

  02 Dec 2020

02 Dec 2020

Review status: this preprint is currently under review for the journal AMT.

Development and Application of a United States wide correction for PM2.5 data collected with the PurpleAir sensor

Karoline K. Barkjohn1, Brett Gantt2, and Andrea L. Clements3 Karoline K. Barkjohn et al.
  • 1ORISE fellow hosted by the Office of Research and Development, U.S. Environmental Protection Agency 109 T.W. Alexander Drive Research Triangle Park, NC 27711
  • 2Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive Research Triangle Park, NC 27711
  • 3Office of Research and Development, U.S. Environmental Protection Agency 109 T.W. Alexander Drive Research Triangle Park, NC 27711

Abstract. PurpleAir sensors which measure particulate matter (PM) are widely used by individuals, community groups, and other organizations including state and local air monitoring agencies. PurpleAir sensors comprise a massive global network of more than 10,000 sensors. Previous performance evaluations have typically studied a limited number of PurpleAir sensors in small geographic areas or laboratory environments. While useful for determining sensor behavior and data normalization for these geographic areas, little work has been done to understand the broad applicability of these results outside these regions and conditions. Here, PurpleAir sensors operated by air quality monitoring agencies are evaluated in comparison to collocated ambient air quality regulatory instruments. In total, almost 12,000 24-hour averaged PM2.5 measurements from collocated PurpleAir sensors and Federal Reference Method (FRM) or Federal Equivalent Method (FEM) PM2.5 measurements were collected across diverse regions of the United States (U.S.), including 16 states. Consistent with previous evaluations, under typical ambient and smoke impacted conditions, the raw data from PurpleAir sensors overestimate PM2.5 concentrations by about 40 % in most parts of the U.S. A simple linear regression reduces much of this bias across most U.S. regions, but adding a relative humidity term further reduces the bias and improves consistency in the biases between different regions. More complex multiplicative models did not substantially improve results when tested on an independent dataset. The final PurpleAir correction reduces the root mean square error (RMSE) of the raw data from 8 µg m−3 to 3 µg m−3 with an average FRM or FEM concentration of 9 µg m−3. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM2.5 measurements across the U.S. in the AirNow Fire and Smoke Map (fire.airnow.gov) and has the potential to be successfully used in other air quality and public health applications.

Karoline K. Barkjohn et al.

 
Status: open (until 27 Jan 2021)
Status: open (until 27 Jan 2021)
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Karoline K. Barkjohn et al.

Karoline K. Barkjohn et al.

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Short summary
Although widely used, air sensors measurements are often biased. In this work we develop a correction with a relative humidity term that reduces the bias and improves consistency between different United States regions. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM2.5 measurements across the U.S. in the AirNow Fire and Smoke Map and has the potential to be successfully used in other air quality and public health applications.
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